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AI-assisted Navier-Stokes research sparks an OpenAI priority dispute

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NYU mathematician Tristan Buckmaster has accused OpenAI researchers of racing towards the same Navier-Stokes result after learning about his collaboration’s progress. OpenAI mathematics lead Sébastien Bubeck denies the claims, leaving a consequential dispute about academic priority, AI-assisted research and access to researchers’ model interactions.

OpenAI Watch analysis

What happened

Buckmaster and mathematician Levent Alpöge announced three proofs on 8 September, including what TechCrunch describes as a preliminary finding concerning the Navier-Stokes existence and smoothness problem. The pair reportedly used both Codex and Claude, with Codex doing most of the AI-assisted work.

Buckmaster says they later learnt that information about their progress had reached OpenAI. He alleges that OpenAI then used a large team and substantial computing resources to pursue the same uncommon route, and that Bubeck proposed removing Alpöge’s credit during efforts to resolve the dispute. Bubeck calls the account false and inflammatory and says he followed academic norms.

The central claims

  • A rare route converged
    Buckmaster says OpenAI pursued the same little-used approach just as his collaboration was finalising its results.
  • The timing is disputed
    He alleges OpenAI’s first prompt came only after information about his work reached the company.
  • Credit became contentious
    Buckmaster says Bubeck proposed removing Alpöge’s name and made comments that Buckmaster interpreted as career threats.
  • OpenAI rejects the account
    Bubeck describes the allegations as false and inflammatory and says a fuller statement will follow.

Why it matters

This is bigger than an untidy argument over who reached the blackboard first. If AI labs increasingly join frontier mathematics, researchers need credible rules for priority, confidential work and the separation between customer interactions and a lab’s own research programme.

The evidential line matters too. Buckmaster says he has not seen OpenAI’s proof and does not know whether his Codex data was used. WittyWires could not independently verify the alleged conversations, timing or data use, so none of those points should be treated as established fact.

Our read

Publish the proofs, prompts, timelines and contribution records. Mathematical priority should be settled with inspectable evidence, not institutional muscle or whoever owns the largest pile of GPUs. Until that record appears, readers should keep the research result, the priority dispute and the data-use suspicion in three separate boxes.

What to watch

  • Bubeck’s promised fuller response and any evidence supporting OpenAI’s chronology.
  • Public release and independent scrutiny of both groups’ proofs.
  • Documentation showing when prompts, compute runs and human contributions occurred.
  • Any clarification from OpenAI about whether Codex interactions informed the competing work.

Discussion spark: What evidence and disclosure rules should decide priority when researchers and AI labs converge on the same mathematical result?

Sources and evidence

OpenAI Watch is independently operated by WittyWires. It is not affiliated with, endorsed by, or operated by OpenAI.

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Update

What changed

Update: the timeline and data question come into focus

Simon Willison’s account adds a much more specific chronology to the priority dispute. He reports that NYU mathematician Tristan Buckmaster and Levent Alpöge, who works for Anthropic, spent almost a year on related problems using Claude and Codex and reached a breakthrough on 15 August.

The OpenAI account reproduced by Willison says the company began its own effort on 1 September after hearing rumours that two Millennium Prize problems had been resolved. OpenAI says its agents arrived at a Navier–Stokes resolution on 5 September, about 88 hours after launch, followed by another 17 hours of Lean formalisation and verification using GPT-6 Astra.

The reported scale is extraordinary: 4.9 million agent messages and about 300 billion output tokens across all attempted problems, including 2.7 million messages and roughly 130 billion tokens on Navier–Stokes alone. Those are OpenAI’s figures, not independently verified measurements.

The thorniest new detail concerns training data. OpenAI reportedly says its researchers and agents did not see Buckmaster and Alpöge’s work before publication and that no specific user data was accessed to solve the problem. It also says it cannot rule out de-identified data derived from their product usage having helped improve its models.

That does not establish that either team’s work trained the system or determined OpenAI’s result. It does explain why the dispute matters beyond academic credit. Researchers using frontier models need a far clearer account of whether private, de-identified or aggregated interactions can later shape systems used by competitors.

The mathematical claims still require expert scrutiny, and the competing priority accounts remain disputed. For now, the useful shift is from vague suspicion to three testable questions: when each approach began, what information moved between people and systems, and precisely how customer research data may influence later models.

Sources and evidence

Independent WittyWires Watcher; not an official account or feed.